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Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP

文献类型： 外文期刊

作者： Li, Yafeng 1 ; Xu, Xingang 1 ; Wu, Wenbiao 1 ; Zhu, Yaohui 2 ; Gao, Lutao 3 ; Jiang, Xiangtai 1 ; Meng, Yang 1 ; Yang, Guijun 1 ; Xue, Hanyu 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

2.Jiangsu Univ, Sch Agr Engn, Zhenjiang 212013, Peoples R China

3.Yunnan Agr Univ, Coll Big Data, Kunming, Yunnan, Peoples R China

关键词： Data preprocessing; Feature selection; Machine learning; Hyperspectral monitoring.

期刊名称：SCIENTIFIC REPORTS （ 影响因子：3.9； 五年影响因子：4.3 ）

ISSN： 2045-2322

年卷期： 2025 年 15 卷 1 期

页码：

收录情况： SCI

摘要： Leaf chlorophyll content (LCC) is a key indicator for assessing the growth of grapes. Hyperspectral techniques have been applied to LCC research. However, quantitative prediction of grape LCC using this technique remains challenging due to baseline drift, spectral peak overlap, and ambiguity in the sensitive spectral range. To address these issues, two typical crop leaf hyperspectral data were collected to reveal the spectral response characteristics of grape LCC using standardization by variables (SNV) and multiple far scattering correction (MSC) preprocessing variations. The sensitive spectral range is determined by Pearson's algorithm, and sensitive features are further extracted within that range using Extreme Gradient Boosting (XGBoost), Recursive Feature Elimination (RFE), and Principal components analysis (PCA). Comparison of the prediction ability of Random Forest Regression (RFR) algorithm, Support Vector Machine Regression (SVR) model, and Genetic Algorithm-Based Neural Network (GA-BP) on grape LCC based on sensitive features. A SNV-RFE-GA-BP framework for predicting hyperspectral LCC in grapes is proposed, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{R}<^>{2}$$\end{document}=0.835 and NRMSE = 0.091. The analysis results show that SNV and MSC treatments improve the correlation between spectral reflectance and LCC, and different feature screening methods have a greater impact on the model prediction accuracy. It was shown that SNV-based processed hyperspectral data combined with GA-BP has great potential for efficient chlorophyll monitoring in grapevine. This method provides a new framework theory for constructing a hyperspectral analytical model of grapevine key growth indicators.

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